


from typing import Any, Dict, Optional

import torch
import torch.nn.functional as F
from torch import nn

from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings






@maybe_allow_in_graph
class _lowerCamelCase(		nn.Module  ):
       def __init__(   self, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase=0.0, lowerCamelCase = None, lowerCamelCase = "geglu", lowerCamelCase = None, lowerCamelCase = False, lowerCamelCase = False, lowerCamelCase = False, lowerCamelCase = False, lowerCamelCase = True, lowerCamelCase = "layer_norm", lowerCamelCase = False, )			->  Dict:




           """simple docstring"""

           super().__init__()
           _lowercase			:				Union[str, Any] 						=						only_cross_attention

           _lowercase			:				Union[str, Any] 						=						(num_embeds_ada_norm is not None) and norm_type == 'ada_norm_zero'
           _lowercase			:				List[str] 						=						(num_embeds_ada_norm is not None) and norm_type == 'ada_norm'

           if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None:
               raise ValueError(
                   F'''`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to'''
                   F''' define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}.''')

           # Define 3 blocks. Each block has its own normalization layer.
           # 1. Self-Attn
           if self.use_ada_layer_norm:
               _lowercase			:				Union[str, Any] 						=						AdaLayerNorm(lowerCamelCase, lowerCamelCase)
           elif self.use_ada_layer_norm_zero:
               _lowercase			:				List[str] 						=						AdaLayerNormZero(lowerCamelCase, lowerCamelCase)
           else:
               _lowercase			:				Tuple 						=						nn.LayerNorm(lowerCamelCase, elementwise_affine=lowerCamelCase)
           _lowercase			:				Optional[int] 						=						Attention(
               query_dim=lowerCamelCase, heads=lowerCamelCase, dim_head=lowerCamelCase, dropout=lowerCamelCase, bias=lowerCamelCase, cross_attention_dim=cross_attention_dim if only_cross_attention else None, upcast_attention=lowerCamelCase, )

           # 2. Cross-Attn
           if cross_attention_dim is not None or double_self_attention:
               # We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
               # I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during
               # the second cross attention block.
               _lowercase			:				Dict 						=						(
                   AdaLayerNorm(lowerCamelCase, lowerCamelCase)
                   if self.use_ada_layer_norm
                   else nn.LayerNorm(lowerCamelCase, elementwise_affine=lowerCamelCase)
               )
               _lowercase			:				str 						=						Attention(
                   query_dim=lowerCamelCase, cross_attention_dim=cross_attention_dim if not double_self_attention else None, heads=lowerCamelCase, dim_head=lowerCamelCase, dropout=lowerCamelCase, bias=lowerCamelCase, upcast_attention=lowerCamelCase, )  # is self-attn if encoder_hidden_states is none
           else:
               _lowercase			:				Optional[int] 						=						None
               _lowercase			:				List[Any] 						=						None

           # 3. Feed-forward
           _lowercase			:				Dict 						=						nn.LayerNorm(lowerCamelCase, elementwise_affine=lowerCamelCase)
           _lowercase			:				Dict 						=						FeedForward(lowerCamelCase, dropout=lowerCamelCase, activation_fn=lowerCamelCase, final_dropout=lowerCamelCase)

           # let chunk size default to None
           _lowercase			:				Union[str, Any] 						=						None
           _lowercase			:				str 						=						0
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase)			->  Tuple:




           """simple docstring"""

           _lowercase			:				Any 						=						chunk_size
           _lowercase			:				Union[str, Any] 						=						dim




       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = None, )			->  List[Any]:




           """simple docstring"""

           if self.use_ada_layer_norm:
               _lowercase			:				List[str] 						=						self.norma(lowerCamelCase, lowerCamelCase)
           elif self.use_ada_layer_norm_zero:
               _lowercase						,							_lowercase						,							_lowercase						,							_lowercase						,							_lowercase			:				Any 						=						self.norma(
                   lowerCamelCase, lowerCamelCase, lowerCamelCase, hidden_dtype=hidden_states.dtype)
           else:
               _lowercase			:				List[Any] 						=						self.norma(lowerCamelCase)

           _lowercase			:				List[Any] 						=						cross_attention_kwargs if cross_attention_kwargs is not None else {}

           _lowercase			:				Any 						=						self.attna(
               lowerCamelCase, encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None, attention_mask=lowerCamelCase, **lowerCamelCase, )
           if self.use_ada_layer_norm_zero:
               _lowercase			:				List[Any] 						=						gate_msa.unsqueeze(1) * attn_output
           _lowercase			:				List[Any] 						=						attn_output + hidden_states

           # 2. Cross-Attention
           if self.attna is not None:
               _lowercase			:				List[Any] 						=						(
                   self.norma(lowerCamelCase, lowerCamelCase) if self.use_ada_layer_norm else self.norma(lowerCamelCase)
               )

               _lowercase			:				Any 						=						self.attna(
                   lowerCamelCase, encoder_hidden_states=lowerCamelCase, attention_mask=lowerCamelCase, **lowerCamelCase, )
               _lowercase			:				Optional[int] 						=						attn_output + hidden_states

           # 3. Feed-forward
           _lowercase			:				List[str] 						=						self.norma(lowerCamelCase)

           if self.use_ada_layer_norm_zero:
               _lowercase			:				str 						=						norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]

           if self._chunk_size is not None:
               # "feed_forward_chunk_size" can be used to save memory
               if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0:
                   raise ValueError(
                       F'''`hidden_states` dimension to be chunked: {norm_hidden_states.shape[self._chunk_dim]} has to be divisible by chunk size: {self._chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`.''')

               _lowercase			:				Optional[Any] 						=						norm_hidden_states.shape[self._chunk_dim] // self._chunk_size
               _lowercase			:				Optional[Any] 						=						torch.cat(
                   [self.ff(lowerCamelCase) for hid_slice in norm_hidden_states.chunk(lowerCamelCase, dim=self._chunk_dim)], dim=self._chunk_dim, )
           else:
               _lowercase			:				Union[str, Any] 						=						self.ff(lowerCamelCase)

           if self.use_ada_layer_norm_zero:
               _lowercase			:				List[str] 						=						gate_mlp.unsqueeze(1) * ff_output

           _lowercase			:				Tuple 						=						ff_output + hidden_states

           return hidden_states






class _lowerCamelCase(		nn.Module  ):
       def __init__(   self, lowerCamelCase, lowerCamelCase = None, lowerCamelCase = 4, lowerCamelCase = 0.0, lowerCamelCase = "geglu", lowerCamelCase = False, )			->  Tuple:




           """simple docstring"""

           super().__init__()
           _lowercase			:				Optional[Any] 						=						int(dim * mult)
           _lowercase			:				Optional[Any] 						=						dim_out if dim_out is not None else dim

           if activation_fn == "gelu":
               _lowercase			:				Union[str, Any] 						=						GELU(lowerCamelCase, lowerCamelCase)
           if activation_fn == "gelu-approximate":
               _lowercase			:				Optional[Any] 						=						GELU(lowerCamelCase, lowerCamelCase, approximate='tanh')
           elif activation_fn == "geglu":
               _lowercase			:				str 						=						GEGLU(lowerCamelCase, lowerCamelCase)
           elif activation_fn == "geglu-approximate":
               _lowercase			:				Union[str, Any] 						=						ApproximateGELU(lowerCamelCase, lowerCamelCase)

           _lowercase			:				List[Any] 						=						nn.ModuleList([])
           # project in
           self.net.append(lowerCamelCase)
           # project dropout
           self.net.append(nn.Dropout(lowerCamelCase))
           # project out
           self.net.append(nn.Linear(lowerCamelCase, lowerCamelCase))
           # FF as used in Vision Transformer, MLP-Mixer, etc. have a final dropout
           if final_dropout:
               self.net.append(nn.Dropout(lowerCamelCase))




       def      UpperCamelCase	(   self, lowerCamelCase)			->  Optional[int]:




           """simple docstring"""

           for module in self.net:
               _lowercase			:				Union[str, Any] 						=						module(lowerCamelCase)
           return hidden_states






class _lowerCamelCase(		nn.Module  ):
       def __init__(   self, lowerCamelCase, lowerCamelCase, lowerCamelCase = "none")			->  Optional[int]:




           """simple docstring"""

           super().__init__()
           _lowercase			:				Union[str, Any] 						=						nn.Linear(lowerCamelCase, lowerCamelCase)
           _lowercase			:				List[Any] 						=						approximate
       def      UpperCamelCase	(   self, lowerCamelCase)			->  Tuple:




           """simple docstring"""

           if gate.device.type != "mps":
               return F.gelu(lowerCamelCase, approximate=self.approximate)
           # mps: gelu is not implemented for float16
           return F.gelu(gate.to(dtype=torch.floataa), approximate=self.approximate).to(dtype=gate.dtype)




       def      UpperCamelCase	(   self, lowerCamelCase)			->  Tuple:




           """simple docstring"""

           _lowercase			:				Optional[int] 						=						self.proj(lowerCamelCase)
           _lowercase			:				Union[str, Any] 						=						self.gelu(lowerCamelCase)
           return hidden_states






class _lowerCamelCase(		nn.Module  ):
       def __init__(   self, lowerCamelCase, lowerCamelCase)			->  int:




           """simple docstring"""

           super().__init__()
           _lowercase			:				Optional[Any] 						=						nn.Linear(lowerCamelCase, dim_out * 2)
       def      UpperCamelCase	(   self, lowerCamelCase)			->  Any:




           """simple docstring"""

           if gate.device.type != "mps":
               return F.gelu(lowerCamelCase)
           # mps: gelu is not implemented for float16
           return F.gelu(gate.to(dtype=torch.floataa)).to(dtype=gate.dtype)




       def      UpperCamelCase	(   self, lowerCamelCase)			->  Optional[int]:




           """simple docstring"""

           _lowercase						,							_lowercase			:				Tuple 						=						self.proj(lowerCamelCase).chunk(2, dim=-1)
           return hidden_states * self.gelu(lowerCamelCase)






class _lowerCamelCase(		nn.Module  ):
       def __init__(   self, lowerCamelCase, lowerCamelCase)			->  Any:




           """simple docstring"""

           super().__init__()
           _lowercase			:				str 						=						nn.Linear(lowerCamelCase, lowerCamelCase)




       def      UpperCamelCase	(   self, lowerCamelCase)			->  Union[str, Any]:




           """simple docstring"""

           _lowercase			:				Optional[int] 						=						self.proj(lowerCamelCase)
           return x * torch.sigmoid(1.7_0_2 * x)






class _lowerCamelCase(		nn.Module  ):
       def __init__(   self, lowerCamelCase, lowerCamelCase)			->  Tuple:




           """simple docstring"""

           super().__init__()
           _lowercase			:				int 						=						nn.Embedding(lowerCamelCase, lowerCamelCase)
           _lowercase			:				List[Any] 						=						nn.SiLU()
           _lowercase			:				Optional[Any] 						=						nn.Linear(lowerCamelCase, embedding_dim * 2)
           _lowercase			:				List[Any] 						=						nn.LayerNorm(lowerCamelCase, elementwise_affine=lowerCamelCase)




       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase)			->  Optional[Any]:




           """simple docstring"""

           _lowercase			:				List[str] 						=						self.linear(self.silu(self.emb(lowerCamelCase)))
           _lowercase						,							_lowercase			:				int 						=						torch.chunk(lowerCamelCase, 2)
           _lowercase			:				Optional[Any] 						=						self.norm(lowerCamelCase) * (1 + scale) + shift
           return x






class _lowerCamelCase(		nn.Module  ):
       def __init__(   self, lowerCamelCase, lowerCamelCase)			->  Union[str, Any]:




           """simple docstring"""

           super().__init__()

           _lowercase			:				List[Any] 						=						CombinedTimestepLabelEmbeddings(lowerCamelCase, lowerCamelCase)

           _lowercase			:				Tuple 						=						nn.SiLU()
           _lowercase			:				Any 						=						nn.Linear(lowerCamelCase, 6 * embedding_dim, bias=lowerCamelCase)
           _lowercase			:				List[str] 						=						nn.LayerNorm(lowerCamelCase, elementwise_affine=lowerCamelCase, eps=1E-6)




       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase=None)			->  Dict:




           """simple docstring"""

           _lowercase			:				Optional[int] 						=						self.linear(self.silu(self.emb(lowerCamelCase, lowerCamelCase, hidden_dtype=lowerCamelCase)))
           _lowercase						,							_lowercase						,							_lowercase						,							_lowercase						,							_lowercase						,							_lowercase			:				List[str] 						=						emb.chunk(6, dim=1)
           _lowercase			:				Dict 						=						self.norm(lowerCamelCase) * (1 + scale_msa[:, None]) + shift_msa[:, None]
           return x, gate_msa, shift_mlp, scale_mlp, gate_mlp






class _lowerCamelCase(		nn.Module  ):
       def __init__(   self, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase = None, lowerCamelCase = 1E-5)			->  Union[str, Any]:




           """simple docstring"""

           super().__init__()
           _lowercase			:				Optional[Any] 						=						num_groups
           _lowercase			:				Any 						=						eps

           if act_fn is None:
               _lowercase			:				Optional[Any] 						=						None
           else:
               _lowercase			:				Any 						=						get_activation(lowerCamelCase)

           _lowercase			:				int 						=						nn.Linear(lowerCamelCase, out_dim * 2)




       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase)			->  Optional[int]:




           """simple docstring"""

           if self.act:
               _lowercase			:				Optional[int] 						=						self.act(lowerCamelCase)
           _lowercase			:				Union[str, Any] 						=						self.linear(lowerCamelCase)
           _lowercase			:				Optional[Any] 						=						emb[:, :, None, None]
           _lowercase						,							_lowercase			:				Optional[Any] 						=						emb.chunk(2, dim=1)

           _lowercase			:				Any 						=						F.group_norm(lowerCamelCase, self.num_groups, eps=self.eps)
           _lowercase			:				Optional[Any] 						=						x * (1 + scale) + shift
           return x



def 			lowercase(	UpperCamelCase_ = 100       )		->	int:







 '''simple docstring'''
 UpperCamelCase			  =      n * (n + 1) * (2 * n + 1) / 6
 UpperCamelCase			  =      (n * (n + 1) / 2) ** 2
 return int(square_of_sum - sum_of_squares       )


if __name__ == "__main__":
     print(F'''{solution() = }''')
